One of the characteristics of modern-day product management is the use of feedback loops in product design and development. The shorter frequency to obtain the feedback, integrate that feedback into the product, and then again get the feedback for the improved versions has enabled product managers and teams to build better products faster and with fewer resources. Product Analytics is a function and technique that makes the feedback loops possible by observing and recording real-time user behavior and patterns. It thus gives an objective overview of what you can do to improve the value proposition.
What Does Product Analytics Mean?
Product Analytics means developing the product metrics to understand the extent of value your product delivers to your customers. It is about objectively understanding the customer journey and the challenges by recording user behavior and actions using product Analytics tools. The insights from such metrics allow you to make product decisions that provide even more value to your customers.
Without the tools, understanding customer behavior and product performance would be subjective. You might collect such product insights through customer interviews, focus groups, or surveys. However, there may be differences between what the customers think they want and what they do or need. Product Analytics help you understand customer behavior throughout the journey and provide actionable insights.
What Are Different Types of Product Analytics Data?
Modern-day product Analytics is about understanding behaviors. Behavioral data identifies the patterns and trends in user behavior, such as which features are most popular, where users drop off, and what problems they encounter. You can use this information to make data-driven decisions, such as improving the user experience, increasing engagement, and identifying monetization opportunities. Additionally, you can also use it to drive customer retention and loyalty. In short, behavioral analytics helps companies to improve their products and better serve their customers.
Behavior analysis requires two types of data points; Object data and Event data.
Object data enables an understanding of the characteristics of entities. For example, suppose you want to analyze user behavior based on age, gender, or geography. In that case, you will need to capture such information through the user profile. Such object data can help you better understand user demographics. The object data can also refer to information about objects on the page that enable user interactions. Such objects can be buttons, links, cards, tables, or other objects.
While object data can help us understand entity characteristics, event data helps us understand how those characteristics impact user behavior. Event data allows you to analyze how the users interact progressively through your product and what actions they take.
You can use event and object data to understand user behavior and how users interact with a product or website. For example, event data can be used to understand user actions. At the same time, object data helps understand the context and details of those actions.
Significant Product Metrics
Acquisition
Customer Acquisition Cost (CAC)
Customer Acquisition Cost (CAC) is a metric used to measure the cost of acquiring a new customer. It represents the total cost of all marketing activities, from advertising and promotions to sales and customer service, divided by the number of customers acquired.
Conversion Rate
Conversion rate is a metric used to measure the percentage of visitors who take a desired action, such as making a purchase or signing up for an email list. Conversion rate is helpful to measure the effectiveness of marketing campaigns and help identify areas for improvement.
Retention
Churn
Churn is a metric used to measure customer attrition or the rate at which customers stop using a product or service. Analyzing churn can identify areas of improvement and inform marketing strategies to reduce customer churn.
Customer Lifetime Value (CLV)
Customer Lifetime Value (CLV) is a metric used to measure the total value of a customer over their lifetime. It takes into account the revenue generated from repeat purchases, as well as any additional benefits such as referrals and brand loyalty. CLV can help businesses prioritize their marketing efforts and focus on acquiring and retaining high-value customers.
Retention Rate
Retention rate is a metric used to measure the percentage of customers who continue to use a product or service over time. This analysis helps identify areas of improvement, inform marketing strategies, and measure the success of customer loyalty programs.
Engagement
Net Promoter Score (NPS)
Net Promoter Score (NPS) is a measure of customer loyalty and satisfaction used in product analytics. It is calculated by asking customers to rate their experience on a scale from 0-10, with 10 being the highest score.
Behavioral Metrics
The above metrics are more business or marketing oriented. From the product perspective, in addition to these metrics, it is essential to focus on behavioral metrics. Let’sLet’s look at some of the vital behavioral metrics you should track.
1. Time To Value
How soon can your users identify the value that your product provides? How soon are they ready to pay for your product services and start realizing that value?
2. Extent of Adoption
Identifying the extent to which your users utilize your product features can provide you with multiple insights. The frequency with which users use certain features can give you an understanding of the product’sproduct’s stickiness. Heavy usage of a feature indicates the value your customers and users derive from your product. On the other hand, the number of features being utilized will give you an idea about the usefulness of the features across your customer base. If there are features that see very minimal or no usage, you would not want to focus your attention there.
This extent can be identified by tracking the number of user sessions and the number of user actions per session. Consider using Dwell time (the time spent on a particular page or screen) to identify the extent of adoption.
3. Conversion from free to paid usage
The conversion rate works at two levels. First, from the acquisition perspective, the conversion rate describes how many users who visit your website or landing page of your product register and start using it. This initial usage may be for a free trial or free services. The second conversion is from free usage to paid usage. Measuring this conversion rate is as critical as measuring the conversion from visitor to a user.
The foundation for all the above metrics is the identification and segmentation of your users on multiple criteria, for example, gender, age, and geographic location, among others. You must identify the correct categories for your user segments based on your product and context.
While this is not an exhaustive list, and there are many other metrics that you can use, these are the minimal metrics that can indicate user behavioral patterns and allow you to identify friction points for your users.
The Types of Analysis
Once you decide what metrics to track, you will need to consider how you will analyze the data. To understand various analysis techniques, you can think of a three-level framework.
1. The topmost level relates to the three factors; two significant events in the case of a customer lifetime from the product perspective; acquisition, engagement, and retention. The third factor is the various experiments or changes you make to the product during the lifecycle and how the customers perceive it.
2. The second level represents how you will analyze the data you have to get insights into the above three factors. There are five significant kinds of analysis you can do;
Trends Analysis.
Trends analysis looks at patterns and changes over time. You can use it to identify trends in customer acquisition, product usage, and engagement. This analysis can help you develop product and marketing strategies and product development decisions.
Journey Analysis
Journey analysis analyses the customer journey through all levels of your marketing funnel, from awareness to acquisition and beyond. From the product perspective, it can provide insights into customer experience, user actions, how customers interact with your product or service, their needs, and which touchpoints are most effective in driving user engagement. Funnel Analysis is a popular type of Journey Analysis.
Attribution Analysis
Attribution analysis allows you to understand customer actions against a specific benchmark. It is used to identify the actions or features responsible for particular results.
Cohort Analysis & Segment Analysis
Cohort Analysis allows you to analyze the behavior of a user group rather than individual users over time.
Cohort Analysis is similar to Segment Analysis. But for the segment analysis, you will group the users on some of their behavioral or demographic characteristics for analysis. Again, there may or may not be a time element involved.
A/B Analysis
The A/B Analysis is where you compare the options to see which works best. Typically, this is used with experimentation. However, depending on your context and the kind of analysis you want to do, you can use it to compare other dimensions as well.
3. The third level is the analysis techniques you will use to get insights from the data. These can be statistical methods (e.g., Multi-variate Testing), machine learning algorithms, or other analysis techniques, depending on your data type and the insights you want to obtain. Product Analytics platforms remove the pain of manually identifying and implementing such methods and algorithms and provide abstractions to get the analysis you need without too much effort. You can make use of such product analytics solutions.
Finally
Product analytics provide a data-driven foundation to build and optimize your product. It helps you understand customer behavior, identify trends, and measure the impact of changes to your product. With the right analytics tools, you can make data-driven decisions that will help you improve user experience and increase engagement with your product, thus enabling your product to grow and deliver even more value to your customers, users, and business.
[…] Data-driven analytics and decision-making allow for objective decisions and overcoming biases and subjective preferences. The data analysis allows the product managers to not only measure product performance but also understand feature usage and needs, prioritize further development, and align the roadmap that helps respond to actual customer pain while helping the business achieve its objectives. […]